DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences

Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In several computational models, conventional protein descriptors are shown to be not informative enough to predict accurate DTIs. Thus, in this study, we employ a convolutional neural network (CNN) on raw protein sequences to capture local residue patterns participating in DTIs. With CNN on protein sequences, our model performs better than previous protein descriptor-based models. In addition, our model performs better than the previous deep learning model for massive prediction of DTIs. By examining the pooled convolution results, we found that our model can detect binding sites of proteins for DTIs. In conclusion, our prediction model for detecting local residue patterns of target proteins successfully enriches the protein features of a raw protein sequence, yielding better prediction results than previous approaches.Comment: 26 pages, 7 figure

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

[Abstract] Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

Machine learning for the prediction of phenols cytotoxicity

Quantitative structure-activity relationships (QSAR) are relevant techniques that assist biologists and chemists in accelerating the drug design process and help understanding many biological and chemical mechanisms. Using classical statistical methods may affect the accuracy and the reliability of the developed QSAR models. This work aims to use a machine learning approach to establish a QSAR model for phenols cytotoxicity prediction. This issue concern many chemists and biologists. In this investigation, the dataset is diverse, and the cytotoxicity data are sparse. Multi-component description of the compounds has then been considered. A set of molecular descriptors fed the deep neural network (DNN) and served to train the DNN. The established DNN model was able to predict the cytotoxicity of the phenols at high precision. The correlation coefficient at the fitting stage was higher than other statistical methods reported in the literature or developed in the present work, specifically multiple linear regression (MLR) and shallow artificial neural networks (ANN), and was equal to 0.943. The predictive capability of the model, as estimated by the coefficient of determination on an external predictive dataset, was significantly high and was about 0.739. This finding could help implement many molecular descriptors relevant to describing the compounds, representing the effects governing the phenols' cytotoxicity toward Tetrahymena pyriformis, avoiding overfitting and outlier exclusion

Artificial Intelligence-Based Drug Design and Discovery

The drug discovery process from hit-to-lead has been a challenging task that requires simultaneously optimizing numerous factors from maximizing compound activity, efficacy to minimizing toxicity and adverse reactions. Recently, the advance of artificial intelligence technique enables drugs to be efficiently purposed in silico prior to chemical synthesis and experimental evaluation. In this chapter, we present fundamental concepts of artificial intelligence and their application in drug design and discovery. The emphasis will be on machine learning and deep learning, which demonstrated extensive utility in many branches of computer-aided drug discovery including de novo drug design, QSAR (Quantitative Structure–Activity Relationship) analysis, drug repurposing and chemical space visualization. We will demonstrate how artificial intelligence techniques can be leveraged for developing chemoinformatics pipelines and presented with real-world case studies and practical applications in drug design and discovery. Finally, we will discuss limitations and future direction to guide this rapidly evolving field

Fusion of molecular representations and prediction of biological activity using convolutional neural network and transfer learning

Basic structural features and physicochemical properties of chemical molecules determine their behaviour during chemical, physical, biological and environmental processes and hence need to be investigated for determining and modelling the actions of the molecule. Computational approaches such as machine learning methods are alternatives to predict physiochemical properties of molecules based on their structures. However, limited accuracy and error rates of these predictions restrict their use. This study developed three classes of new methods based on deep learning convolutional neural network for bioactivity prediction of chemical compounds. The molecules are represented as a convolutional neural network (CNN) with new matrix format to represent the molecular structures. The first class of methods involved the introduction of three new molecular descriptors, namely Mol2toxicophore based on molecular interaction with toxicophores features, Mol2Fgs based on distributed representation for constructing abstract features maps of a selected set of small molecules, and Mol2mat, which is a molecular matrix representation adapted from the well-known 2D-fingerprint descriptors. The second class of methods was based on merging multi-CNN models that combined all the molecular representations. The third class of methods was based on automatic learning of features using values within the neurons of the last layer in the proposed CNN architecture. To evaluate the performance of the methods, a series of experiments were conducted using two standard datasets, namely MDL Drug Data Report (MDDR) and Sutherland datasets. The MDDR datasets comprised 10 homogeneous and 10 heterogeneous activity classes, whilst Sutherland datasets comprised four homogeneous activity classes. Based on the experiments, the Mol2toxicophore showed satisfactory prediction rates of 92% and 80% for homogeneous and heterogeneous activity classes, respectively. The Mol2Fgs was better than Mol2toxicophore with prediction accuracy result of 95% for homogeneous and 90% for heterogeneous activity classes. The Mol2mat molecular representation had the highest prediction accuracy with 97% and 94% for homogeneous and heterogeneous datasets, respectively. The combined multi-CNN model leveraging on the knowledge acquired from the three molecular presentations produced better accuracy rate of 99% for the homogeneous and 98% for heterogeneous datasets. In terms of molecular similarity measure, use of the values in the neurons of the last hidden layer as the automatically learned feature in the multi-CNN model as a novel molecular learning representation was found to perform well with 88.6% in terms of average recall value in 5% structures most similar to the target search. The results have demonstrated that the newly developed methods can be effectively used for bioactivity prediction and molecular similarity searching

Analyzing Learned Molecular Representations for Property Prediction

Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors, and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 16 proprietary industrial datasets spanning a wide variety of chemical endpoints. In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows